Cell density quantification with TurboSPI: R_2~* mapping with compensation for off-resonance fat modulation

摘要

Objective Tracking the migration of superparamagnetic iron oxide (SPIO)-labeled immune cells in vivo is valuable for understanding the immunogenic response to cancer and therapies.Quantitative cell tracking using TurboSPI-based R_2~* mapping is a promising development to improve accuracy in longitudinal studies on immune recruitment.However,offresonance fat signal isochromats lead to modulations in the signal time-course that can be erroneously fit as R_2~* signal decay,overestimating the density of labeled cells,while excluding voxels with fat-typical modulations results in underestimation of cell density in voxels with mixed content.Approaches capable of accurate R_2~* estimation in the presence of fat are needed.Methods We propose a dual-decay (separate R_(2f)~* and R_(2w)~* for fat and water) Dixon-based signal model that accounts for the presence of fat in a voxel to provide better estimates of SPIO-induced dephasing.This model was tested in silico,in phantoms with varying quantities of fat and SPIO-labeled cells,and in 5 mice injected with SPIO-labeled CD8+ T cells.Results In silico single voxel simulations illustrate how the proposed dual-decay model provides stable R_(2w)~* estimates that are invariant to fat content.The proposed model outperforms previous methods when applied to in vitro samples of SPIOlabeled cells and oil prepared with oil content ≥ 15%.Preliminary in vivo results show that,compared to previous methods,the dual-decay model improves the balance of R_2~* mapping in fat-dense areas,which will yield more reliable analysis in future cell tracking studies.Discussion The proposed model is a promising tool for quantitative TurboSPI R_2~* cell tracking,with further refinements offering the possibility of better specificity and sensitivity.